An exciting career awaits you

At MPC, we’re committed to being a great place to work – one that welcomes new ideas, encourages diverse perspectives, develops our people, and fosters a collaborative team environment.

We are seeking a highly skilled Data Scientist focused on Machine Learning and MLOps to join our dynamic team.  In this role, you will independently handle medium complexity data science projects, engaging with stakeholders to understand requirements and design optimized machine learning models.  You will apply advanced statistical methods to extract actionable insights from large datasets, collaborate with Business Intelligence professionals, and develop custom data algorithms.  Your expertise will be crucial in deploying models into production environments, providing peer reviews, and advising on best practices in data collection, model training, and MLOps.  Additionally, you will have the opportunity to contribute to high complexity projects under the guidance of Senior Data Scientists.  This position offers the opportunity to contribute significantly to our data-driven decision-making processes and drive innovation within the organization.

This position belongs to a family of jobs with increasing responsibility, competency, and skill level.  Actual position title and pay grade will be based on the selected candidate’s experience and qualifications.

• Independently handles medium complexity data science projects.
• Contribute to high complexity projects under the guidance of Senior Data Scientists.
• Engages with stakeholders to understand project requirements.
• Designs, tests, and optimizes machine learning models.
• Applies advanced statistical methods to extract insights from data.
• Collaborates with Business Intelligence professionals to translate insights into business recommendations.
• Uses big data technologies to handle and process large datasets.
• Develops custom data algorithms and models for specific business needs.
• Provides support in deploying models in production environments.
• Contributes to peer reviews and code assessments.
• Advises on best practices in data collection and model training.

• Bachelor’s Degree in Information Technology, relevant field or equivalent experience.
• 2+ years of relevant experience required

A/B Testing - A/B testing, also known as split testing, is a method of comparing two versions of a webpage, app, email, or other digital asset to determine which one performs better. In an A/B test, two variants—A and B—are compared by randomly assigning users to either version and analyzing their behavior to determine which variant is more effective. The goal is often to optimize for a specific metric, such as click-through rate, conversion rate, or engagement.

Artificial Intelligence (AI) - Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI encompasses a broad range of techniques and approaches, including machine learning, neural networks, natural language processing, computer vision, robotics, and expert systems, among others. The ultimate goal of AI is to create machines that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, making decisions, and solving problems.

Authentic Communicator - Expresses ideas and information, both verbally and in writing, clearly and credibly. Listens to understand and fosters constructive dialogue.

Business Acumen - Applies knowledge of MPC’s business, industry and the marketplace to advance the organization’s goals. Makes decisions and recommendations clearly linked to MPC’s strategy.

Data Analysis - The process of measuring and managing organizational data, identifying methodological best practices and conducting statistical analyses.

Data Mining - Data mining is sorting through data to identify patterns and establish relationships. Data mining parameters include: Association - looking for patterns where one event is connected to another event Sequence or path

analysis - looking for patterns where one event leads to another later event Classification - looking for new patterns Clustering - finding and visually documenting groups of facts not previously known Forecasting - discovering patterns in data that can lead to reasonable predictions about the future Data mining techniques are used in mathematics, cybernetics, and genetics. Web mining, a type of data mining used in customer relationship management [CRM], takes advantage of the huge amount of information gathered by a Web site to look for patterns in user behavior.

Data Preparation - Data preparation, data wrangling, also known as data munging, refers to the process of cleaning, structuring, and transforming raw data into a suitable format for analysis or other purposes. It involves tasks such as

handling missing values, standardizing formats, and merging datasets to ensure data quality and usability.

Data Visualization - Skill in designing and creating compelling visualizations and data stories that effectively communicate insights and facilitate decision-making.

Deep Analytics - Deep analytics involves sophisticated methods and tools to analyze complex datasets, extracting valuable insights and patterns that go beyond traditional statistical approaches. These techniques often include machine

learning algorithms, predictive modeling, data mining, and other advanced statistical methods, enabling organizations to gain deeper understanding, make informed decisions, and uncover hidden relationships within their data. Advanced analytics goes beyond descriptive analytics by predicting future trends, optimizing processes, and providing actionable intelligence for strategic decision-making.

Deep Learning - A subset of machine learning involving neural networks with deep layers, enabling the model to learn and make intelligent decisions from large, complex datasets.

Ethics & Privacy in AI - The practice of ensuring ethical considerations and privacy compliance are integrated into AI and machine learning projects, including issues related to data use, model bias, and transparency.

Machine Learning - Machine learning is a branch of Artificial Intelligence (AI) and computer science that involves the development of algorithms and statistical models that enable computers to progressively improve their performance on a specific task through learning from data, without being explicitly programmed. In essence, machine learning algorithms learn from patterns and relationships in data to make predictions, decisions, or identify patterns, often with the goal of optimizing some objective function or improving performance over time.

Machine Learning Operations - Machine learning techniques involve the use of algorithms and statistical models to enable computer systems to improve their performance on a specific task over time, without being explicitly programmed. These techniques encompass a range of approaches, including supervised learning, unsupervised learning, and reinforcement learning, to extract patterns and insights from data for making predictions or decisions.

Natural Language Processing (NLP) - Proficiency in analyzing and extracting insights from unstructured text data, including sentiment analysis, topic modeling, and language understanding.

Predictive Modeling - Techniques used to predict future outcomes based on historical data, using statistical algorithms and machine learning techniques.

Responsible Innovation - Knowledge of ethical considerations related to data usage, data-driven technologies and strategies to mitigate biases in data-driven decision-making.

Statistical Analysis - Statistical Analysis is used in support of decision-making and includes fundamental principles such as data collection and sampling, random variable types and probability distributions, sampling and population

distributions, making estimations from samples, hypothesis testing, and statistical process control.

As an energy industry leader, our career opportunities fuel personal and professional growth.

Location:

Findlay, Ohio

Additional locations:

San Antonio, Texas

Job Requisition ID:

00012796

Location Address:

539 S Main St

Education:

Employee Group:

Full time

Employee Subgroup:

Regular

Marathon Petroleum Company LP is an Equal Opportunity Employer and gives consideration for employment to qualified applicants without discrimination on the basis of race, color, religion, creed, sex, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), sexual orientation, gender identity, gender expression, reproductive health decision-making, age, mental or physical disability, medical condition or AIDS/HIV status, ancestry, national origin, genetic information, military, veteran status, marital status, citizenship  or any other status protected by applicable federal, state, or local laws.  If you would like more information about your EEO rights as an applicant, click here.

If you need a reasonable accommodation for any part of the application process at Marathon Petroleum LP, please contact our Human Resources Department at talentacquisition@marathonpetroleum.com. Please specify the reasonable accommodation you are requesting, along with the job posting number in which you may be interested. A Human Resources representative will review your request and contact you to discuss a reasonable accommodation. Marathon Petroleum offers a total rewards program which includes, but is not limited to, access to health, vision, and dental insurance, paid time off, 401k matching program, paid parental leave, and educational reimbursement. Detailed benefit information is available at https://mympcbenefits.com.The hired candidate will also be eligible for a discretionary company-sponsored annual bonus program.
 

Equal Opportunity Employer: Veteran / Disability